NP03, a Microdose Lithium Formulation, Blunts Early Amyloid Post-Plaque Neuropathology in McGill-R-Thy1-APP Alzheimer-Like Transgenic Rats
Bibliographic record
Abstract
Epidemiological, preclinical, and clinical studies have suggested a role for microdose lithium in reducing Alzheimer's disease (AD) risk by modulating key mechanisms associated with AD pathology. The novel microdose lithium formulation, NP03, has disease-modifying effects in the McGill-R-Thy1-APP transgenic rat model of AD-like amyloidosis at pre-plaque stages, before frank amyloid-β (Aβ) plaque deposition, during which Aβ is primarily intraneuronal. Here, we are interested in determining whether the positive effects of microdose lithium extend into early Aβ post-plaque stages. We administered NP03 (40μg Li/kg; 1 ml/kg body weight) to McGill-R-Thy1-APP transgenic rats for 12 weeks spanning the transition phase from plaque-free to plaque-bearing. The effect of NP03 on remote working memory was assessed using the novel object recognition task. Levels of human Aβ38, Aβ40, and Aβ42 as well as levels of pro-inflammatory mediators were measured in brain-extracts and plasma using electrochemiluminescent assays. Mature Aβ plaques were visualized with a thioflavin-S staining. Vesicular acetylcholine transporter (VAChT) bouton density and levels of chemokine (C-X-C motif) ligand 1 (CXCL1), interleukin-6 (IL-6), and 4-hydroxynonenal (4-HNE) were probed using quantitative immunohistochemistry. During the early Aβ post-plaque stage, we find that NP03 rescues functional deficits in object recognition, reduces loss of cholinergic boutons in the hippocampus, reduces levels of soluble and insoluble cortical Aβ42 and reduces hippocampal Aβ plaque number. In addition, NP03 reduces markers of neuroinflammation and cellular oxidative stress. Together these results indicate that microdose lithium NP03 is effective at later stages of amyloid pathology, after appearance of Aβ plaques.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".